Current Medical Imaging - Volume 21, Issue 1, 2025

Prenatal intervention with fetoscopic endoluminal tracheal occlusion (FETO) using a balloon can stimulate lung growth and improve neonatal survival for moderate and severe congenital diaphragmatic hernia (CDH). Quantitative parameters measured on magnetic resonance imaging (MRI) can guide the treatment of CDH and evaluate changes after FETO treatment.

We reported on five cases of isolated left congenital diaphragmatic hernia (CDH) in fetuses who underwent FETO surgery. We conducted a comparison of the MRI images before and after FETO treatment and analyzed the correlation between the observed changes and the clinical outcomes of the neonates after delivery.

MRI can precisely provide the anatomical details of CDH and quantitatively analyze changes in fetal lung volume before and after FETO surgery.

Cytotoxic lesions of the corpus callosum (CLOCC) are a rare clinical-radiological syndrome with an unclear specific pathogenesis, and cases occurring consecutively in siblings are exceptionally uncommon. This study reports two pediatric siblings with CLOCC (one experiencing two episodes), highlighting the potential role of genetic susceptibility in its pathogenesis. The findings contribute to the limited literature on familial CLOCC and recurrent cases in children.

Two brothers (aged 9 and 12) presented with sudden-onset aphasia and unilateral limb weakness, preceded by rhinorrhea. Magnetic resonance imaging (MRI) revealed reversible lesions in the splenium of the corpus callosum and bilateral frontoparietal white matter, consistent with CLOCC. Both patients received immunomodulatory therapy (e.g., corticosteroids, intravenous immunoglobulin) and symptomatic treatment, achieving full neurological recovery within approximately one week. The elder sibling had a recurrence two years later (when the patient was 14 years old) with similar imaging findings. Laboratory tests ruled out common infections, and cerebrospinal fluid analysis was unremarkable.

This case underscores CLOCC as a heterogeneous condition with possible genetic predisposition, as evidenced by its occurrence in siblings and recurrence in one sibling. While prognosis is generally favorable, the observed sibling clustering and individual recurrence suggest the need for further research into underlying genetic or immunological mechanisms.

Incontinentia Pigmenti (IP) is a rare X-linked dominant neurocutaneous disorder characterized by cutaneous, ocular, and neurological manifestations. We present a fatal case of IP with atypical neuroimaging findings.

A 4-month-old female infant presented with generalized hyperpigmentation, palatal cleft, and acute encephalopathy. Initial non-contrast cranial Computed Tomography (CT) demonstrated cerebellar hypoattenuation with punctate calcifications and ventriculomegaly. Subsequent Magnetic Resonance Imaging (MRI) demonstrated extensive ischemia, edema, and hemorrhagic lesions in the brainstem, cerebellum, and cervical spinal cord. Trio-based whole-exome sequencing did not detect pathogenic variants in the Inhibitor of Nuclear Factor Kappa-B Kinase Regulatory Subunit Gamma (IKBKG) gene (NM_003639.3).

This case highlights the critical role of neuroimaging in diagnosing IP-related neurological complications and emphasizes the need for early multimodal imaging evaluation. The discordance between clinical phenotype and genetic findings warrants further investigation into novel pathogenic mechanisms.

Gynecological assessment of single females in some countries, where transvaginal ultrasound can not be performed, presents a challenge. This study proposed using computer-assisted analysis (ImageJ software) to assess its feasibility for endometrial analysis and consequently enhance the diagnostic value of transabdominal ultrasound.

This pilot normative cohort study was conducted among 20 single healthy volunteers recruited at Princess Nourah University (PNU) ultrasound lab from November 2022 to April 2023. Participants were followed throughout their entire menstrual cycle and underwent a transabdominal ultrasound in the 4 menstrual phases. Sonographs were analyzed using the ImageJ program, and the data were analyzed with SPSS software.

The mean age of the participants was 21 years (± 0.9), and the average menstrual cycle length was 29.65 days (±2.18). The endometrium measured 0.33 cm (±0.137), 0.63 cm (±0.172), 0.89 cm (±0.167), and 1.06 cm (±0.19) in the menstrual, early proliferative, late proliferative, and secretory phases, respectively. At the same time, the intensity score was 96.735 (±26.24), 117.4 (±27.8), 145.37 (±30.0137), and 157.3 (±21.3) in these phases. Endometrial thickness also showed a moderate positive correlation with the intensity score (r=0.545, p=0.000).

These findings, which demonstrate a correlation between the intensity score and endometrial thickness, underscore the importance of this study in providing a basis for using ImageJ software to analyze transabdominal ultrasound.

This pilot study generated preliminary reference values for endometrial thickness and intensity score using transabdominal ultrasound. It also demonstrated a correlation between these measurements, underscoring the potential utility of ImageJ analysis.

Early detection of gastric cancer remains challenging for many of the current imaging techniques. Recent advancements in reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) have shown promise in improving the visualization of small anatomical structures. This study aimed to evaluate and compare the diagnostic performance of rFOV DWI with multi-detector computed tomography (MDCT) and conventional full field of view (fFOV) DWI for detecting early gastric cancer (EGC).

This retrospective study included 43 patients with pathologically confirmed EGC. All participants underwent pre-treatment imaging, including CT scans and MRI with a prototype rFOV DWI and conventional fFOV DWI at 3 Tesla. Quantitative (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and qualitative (subjective image quality) assessments were performed. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and area-under-the-curve (AUC) analysis.

rFOV DWI demonstrated significantly higher SNR and CNR compared with fFOV DWI (P < 0.05). Subjective image quality scores were also superior for rFOV DWI (P < 0.05). In lesion detection, rFOV DWI showed higher sensitivity (0.705) than CT (0.636) and fFOV DWI (0.523). ROC analysis revealed that rFOV DWI had a higher AUC (0.829, 95% CI [0.764, 0.882]) than fFOV DWI (0.734, 95% CI [0.661, 0.798], P = 0.02) and a modest improvement over CT (0.799, 95% CI [0.731, 0.856], P = 0.51).

The findings suggest that rFOV DWI provides superior image quality and diagnostic accuracy for EGC detection compared with conventional fFOV DWI. While it showed a trend toward better performance than CT, further studies with larger cohorts are needed to validate these results.

rFOV DWI offers improved image quality and diagnostic performance for early gastric cancer detection compared with fFOV DWI, with a potential advantage over CT. This technique may enhance early diagnosis and clinical decision-making in gastric cancer management.

This study aimed to investigate nerve fiber bundle damage associated with spinocerebellar degeneration, a dominant inherited neurological disorder, using magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI).

Four cases of spinocerebellar degeneration and ten matched healthy subjects were retrospectively enrolled. DTI software was used for processing and analysis.

All patients had an abnormal spinocerebellar ataxia (SCA) type 3 gene mutation, with cerebellar and brainstem atrophy, a decreased signal in the pons and projection fibers. Significant interruption and destruction were revealed in the midline of the cerebellar peduncle, cerebellar arcuate fibers, and the spinothalamic and spinocerebellar tracts. Significant (p <0.05) decreases were detected in FA values in the cerebellar peduncle (0.51±0.04 vs. 0.68±0.02), cerebellar arcuate fibers (0.37±0.08 vs. 0.51±0.05), spinothalamic tract (0.42±0.03 vs. 0.49±0.05), and spinocerebellar tract (0.44±0.06 vs. 0.52±0.06) compared with healthy controls. Compared with healthy controls, significant (p <0.05) increases were detected in ADC values in the cerebellar peduncle (0.84±0.11 vs. 0.67±0.03), cerebellar arcuate fibers (0.87±0.12 vs. 0.66±0.05), spinothalamic tract (0.89±0.13 vs. 0.70±0.03) within the brainstem, and spinocerebellar tract (0.79±0.07 vs. 0.69±0.06).

The MRI DTI technique provides sufficient information for studying spinocerebellar degeneration and for conducting further research on its etiology and diagnosis. Some limitations were present, including the retrospective and single-center study design, a limited patient sample, and enrollment of only Chinese patients.

The MRI DTI technique can clearly demonstrate the degree of damage to nerve fiber bundles in the cerebellum and the adjacent relationship between the fiber bundles entering and exiting the cerebellum in patients with spinocerebellar degeneration.

Accurate segmentation of small and irregular pulmonary nodules remains a significant challenge in lung cancer diagnosis, particularly in complex imaging backgrounds. Traditional U-Net models often struggle to capture long-range dependencies and integrate multi-scale features, limiting their effectiveness in addressing these challenges. To overcome these limitations, this study proposes an enhanced U-Net hybrid model that integrates multiple attention mechanisms to enhance feature representation and improve the precision of segmentation outcomes.

The assessment of the proposed model was conducted using the LUNA16 dataset, which contains annotated CT scans of pulmonary nodules. Multiple attention mechanisms, including Spatial Attention (SA), Dilated Efficient Channel Attention (Dilated ECA), Convolutional Block Attention Module (CBAM), and Squeeze-and-Excitation (SE) Block, were integrated into a U-Net backbone. These modules were strategically combined to enhance both local and global feature representations. The model’s architecture and training procedures were designed to address the challenges of segmenting small and irregular pulmonary nodules.

The proposed model achieved a Dice similarity coefficient of 84.30%, significantly outperforming the baseline U-Net model. This result demonstrates improved accuracy in segmenting small and irregular pulmonary nodules.

The integration of multiple attention mechanisms significantly enhances the model’s ability to capture both local and global features, addressing key limitations of traditional U-Net architectures. SA preserves spatial features for small nodules, while Dilated ECA captures long-range dependencies. CBAM and SE further refine feature representations. Together, these modules improve segmentation performance in complex imaging backgrounds. A potential limitation is that performance may still be constrained in cases with extreme anatomical variability or low-contrast lesions, suggesting directions for future research.

The Enhanced U-Net hybrid model outperforms the traditional U-Net, effectively addressing challenges in segmenting small and irregular pulmonary nodules within complex imaging backgrounds.

Accurate liver volumetry is crucial for hepatectomy. In this study, we developed and validated a deep learning system for automated liver volumetry in patients undergoing hepatectomy, both preoperatively and at 7 days and 3 months postoperatively.

A 3D U-Net model was trained on CT images from three time points using a five-fold cross-validation approach. Model performance was assessed with standard metrics and comparatively evaluated across the time points.

The model achieved a mean Dice Similarity Coefficient (DSC) of 94.31% (preoperative: 94.91%; 7-day post-operative: 93.45%; 3-month post-operative: 94.57%) and a mean recall of 96.04%. The volumetric difference between predicted and actual volumes was 1.01 ± 0.06% preoperatively, compared to 1.04 ± 0.03% at other time points (p < 0.05).

This study demonstrates a novel capability to automatically track post-hepatectomy regeneration using AI, offering significant potential to enhance surgical planning and patient monitoring. A key limitation, however, was that the direct correlation with clinical outcomes was not assessed due to constraints of the current dataset. Therefore, future studies using larger, multi-center datasets are essential to validate the model's clinical and prognostic utility.

The developed artificial intelligence model successfully and accurately measured liver volumes across three critical post-hepatectomy time points. These findings support the use of this automated technology as a precise and reliable tool to assist in surgical decision-making and postoperative assessment, providing a strong foundation for enhancing patient care.

Heart failure (HF) is classified into three types based on left ventricular ejection fraction (LVEF). A newly developed transthoracic three-dimensional (3D) echocardiography Heart-Model (HM) offers quick analysis of the volume and function of the left atrium (LA) and left ventricle (LV). This study aimed to determine the value of the HM in HF patients.

A total of 117 patients with HF were divided into three groups according to EF: preserved EF (HFpEF, EF ≥50%), mid-range EF (HFmrEF, EF =41%–49%), and reduced EF (HFrEF, EF ≤40%). The HM was applied to analyze 3D cardiac functional parameters. LVEF was obtained using Simpson’s biplane method. The N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentration was measured.

Significant differences in age, female proportion, body mass index, and comorbidities were observed among the three groups. With decreasing EF across the groups, the 3D volumetric parameters of the LA and LV increased, while LVEF decreased. The LV E/e' was significantly higher in HFrEF patients than in HFpEF patients. LVEF measurement was achieved in significantly less time with the HM compared with the conventional Simpson’s biplane method. The NT-proBNP concentration increased in the following pattern: HFrEF > HFmrEF > HFpEF. The NT-proBNP concentration correlated positively with LV volume and negatively with LVEF from both the HM and Simpson’s biplane method.

LA and LV volumes increase, and the derived LV systolic function decreases with increasing HF severity determined by the HM. The functional parameters measurements provided by the HM are associated with laboratory indicators, indicating the feasibility of using the HM in routine clinical application.

Deep learning methods were employed to perform harmonization analysis on whole-brain scans obtained from 1.5-T and 3.0-T scanners, aiming to increase comparability between different magnetic resonance imaging (MRI) scanners.

Thirty patients evaluated in Beijing Tsinghua Changgung Hospital between August 2020 and March 2023 were included in this retrospective study. Three MRI scanners were used to scan patients, and automated brain image segmentation was performed to obtain volumes of different brain regions. Differences in regional volumes across scanners were analyzed using repeated-measures analysis of variance. For regions showing significant differences, super-resolution deep learning was applied to enhance consistency, with subsequent comparison of results. For regions still exhibiting differences, the Intraclass Correlation Coefficient (ICC) was calculated and the consistency was evaluated using Cicchetti's criteria.

Average whole-brain volumes for different scanners among patients were 1152.36mm³ (SD = 95.34), 1136.92mm³ (SD = 108.21), and 1184.00mm³ (SD = 102.78), respectively. Analysis revealed significant variations in all 12 brain regions (p<0.05), indicating a lack of comparability among imaging results obtained from different magnetic field strengths. After deep learning-based consistency optimization, most brain regions showed no significant differences, except for six regions where differences remained significant. Among these, three regions demonstrated ICC values of 0.868 (95%CI 0.771-0.931), 0.776 (95%CI 0.634-0.877), and 0.893 (95%CI 0.790-0.947), indicating high reproducibility and comparability.

This study demonstrates a deep learning-based harmonization method that effectively mitigates field strength-related inconsistencies between 1.5-T and 3.0-T MRI, significantly enhancing their comparability. The high ICCs observed in key brain regions confirm the robustness of this approach, paving the way for reliable clinical application across different scanners. A noted limitation is its current focus on brain imaging, which warrants future research to extend its applicability to other anatomical areas.

This study employed a novel machine learning approach that significantly improved the comparability of imaging results from patients using different magnetic field strengths and various models of MRI scanners. Furthermore, it enhanced the consistency of central nervous system image segmentation.

This study aimed to evaluate the effectiveness of intelligent quick magnetic resonance (IQMR) for accelerating brain MRI scanning and improving image quality in patients with acute ischemic stroke.

In this prospective study, 58 patients with acute ischemic stroke underwent head MRI examinations between July 2023 and January 2024, including diffusion-weighted imaging and both conventional and accelerated T1-weighted, T2-weighted, and T2 fluid-attenuated inversion recovery fat-saturated (T2-FLAIR) sequences. Accelerated sequences were processed using IQMR, producing IQMR-T1WI, IQMR-T2WI, and IQMR-T2-FLAIR images. Image quality was assessed qualitatively by two readers using a five-point Likert scale (1 = non-diagnostic to 5 = excellent). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of lesions and surrounding tissues were quantitatively measured. The Alberta Stroke Program Early CT Score (ASPECTS) was used to evaluate ischemia severity.

Total scan time was reduced from 5 minutes 9 seconds to 2 minutes 40 seconds, accounting for a reduction of 48.22%. IQMR significantly improved SNR/CNR in accelerated sequences (P < .05), achieving parity with routine sequences (P > .05). Qualitative scores for lesion conspicuity and internal display improved post-IQMR (P < .05).. ASPECTS showed no significant difference between IQMR and routine images (P = 0.79; ICC = 0.91–0.93).

IQMR addressed MRI’s slow scanning limitation without hardware modifications, enhancing diagnostic efficiency. The results have been found to align with advancements in deep learning. Limitations included the small sample size and the exclusion of functional sequences.

IQMR could significantly reduce brain MRI scanning time and enhance image quality in patients with acute ischemic stroke.

This case highlights the rare but life-threatening complication of carotid blowout syndrome (CBS) after radiotherapy for nasopharyngeal carcinoma (NPC). It is characterized by rupture of the carotid artery, often occurring months or years after treatment. Early diagnosis and timely intervention are essential to improve clinical outcomes.

A 45-year-old woman with NPC developed recurrent epistaxis 31 months after chemoradiotherapy. MRI and MRA ruled out tumor recurrence. High-resolution vessel wall imaging (VWI) revealed eccentric thickening, irregular enhancement, and a pseudoaneurysm in the lacerum segment of the left internal carotid artery (ICA), which was confirmed by CTA and DSA. The patient underwent embolization and remained stable at 1-year follow-up.

This case underscores the value of VWI in detecting CBS-related vascular changes. Imaging is crucial for early diagnosis and timely intervention in high-risk patients with NPC who have undergone radiotherapy.

The pool sign, an emerging MRI biomarker for differentiating brain metastases (BM) from primary neoplasms, is primarily documented in case reports. Systematic data on its prevalence and determinants in BM among patients with lung cancer are lacking. This study aims to evaluate the occurrence of the pool sign and identify factors associated with its presence.

Between January 2017 and August 2024, data from 6,004 lung cancer patients were retrospectively extracted from the electronic health records system. The clinical and demographic characteristics, along with BM MRI features, were compared between the pool sign and non-pool sign groups using univariate and multivariate analyses.

A total of 427 patients (81 women; mean age, 62.17 years) were enrolled in the study. The pool sign was observed in 29 patients (6.8%). The inter-reader reliability for the pool sign ranged from moderate to substantial (κ=0.61–0.80), while the intra-reader reliability was moderate (κ=0.6). In the univariate analysis, a statistically significant difference was observed in the volume size of metastases between the pool sign group and the non-pool sign group (median 4.8 vs. 0.5, P < 0.0001). This finding suggests that the presence of the pool sign is more likely associated with BMs exhibiting relatively larger tumor volumes. Additionally, the prevalence of solid-cystic masses was significantly higher in the pool sign group compared to the non-pool sign group, with rates of 79.3% and 44.5%, respectively (P = 0.0014). However, there were no statistically significant differences in other examined variables. In the multivariate analysis, the findings demonstrated that an increase in tumor volume (OR = 1.050, 95% CI 1.025-1.076, P < 0.001) and the presence of a solid-cystic mass (OR = 3.666, 95% CI 1.159-11.595, P = 0.027) were significantly correlated with a higher probability of pool sign occurrence.

The pool sign occurs in 6.8% of BM in patients with lung cancer and is independently associated with larger lesion volume and solid-cystic morphology. Its diagnostic utility warrants further validation.

This study aimed to explore the value of a machine learning model based on spectral computed tomography (CT) for predicting the programmed death ligand-1 (PD-L1) expression in resectable non-small cell lung cancer (NSCLC).

In this retrospective study, 131 instances of NSCLC who underwent preoperative spectral CT scanning were enrolled and divided into a training cohort (n = 92) and a test cohort (n = 39). Clinical-imaging features and quantitative parameters of spectral CT were analyzed. Variable selection was performed using univariate and multivariate logistic regression, as well as LASSO regression. We used eight machine learning algorithms to construct a PD-L1 expression predictive model. We utilized sensitivity, specificity, accuracy, calibration curve, the area under the curve (AUC), F1 score and decision curve analysis (DCA) to evaluate the predictive value of the model.

After variable selection, cavitation, ground-glass opacity, and CT40keV and CT70keV at venous phase were selected to develop eight machine learning models. In the test cohort, the extreme gradient boosting (XGBoost) model achieved the best diagnostic performance (AUC = 0.887, sensitivity = 0.696, specificity = 0.937, accuracy = 0.795 and F1 score = 0.800). The DCA indicated favorable clinical utility, and the calibration curve demonstrated the model’s high level of prediction accuracy.

Our study indicated that the machine learning model based on spectral CT could effectively evaluate the PD-L1 expression in resectable NSCLC.

The XGBoost model, integrating spectral CT quantitative parameters and imaging features, demonstrated considerable potential in predicting PD-L1 expression.

Lung cancer remains a leading cause of cancer-related mortality worldwide. While deep learning approaches show promise in medical imaging, comprehensive comparisons of classifier combinations with DenseNet architectures for lung cancer classification are limited.

The study investigates the performance of different classifier combinations, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Multi-Layer Perceptron (MLP), with DenseNet architectures for lung cancer classification using chest CT scan images.

A comparative analysis was conducted on 1,000 chest CT scan images comprising Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and normal tissue samples. Three DenseNet variants (DenseNet-121, DenseNet-169, DenseNet-201) were combined with three classifiers: SVM, ANN, and MLP. Performance was evaluated using accuracy, Area Under the Curve (AUC), precision, recall, specificity, and F1-score with an 80-20 train-test split.

The optimal model achieved 92% training accuracy and 83% test accuracy. Performance across models ranged from 81% to 92% for training accuracy and 73% to 83% for test accuracy. The most balanced combination demonstrated robust results (training: 85% accuracy, 0.99 AUC; test: 79% accuracy, 0.95 AUC) with minimal overfitting.

Deep learning approaches effectively categorize chest CT scans for lung cancer detection. The MLP-DenseNet-169 combination's 83% test accuracy represents a promising benchmark. Limitations include retrospective design and a limited sample size from a single source.

This evaluation demonstrates the effectiveness of combining DenseNet architectures with different classifiers for lung cancer CT classification. The MLP-DenseNet-169 achieved optimal performance, while SVM-DenseNet-169 showed superior stability, providing valuable benchmarks for automated lung cancer detection systems.

Advancements in computational methods in medicine have brought about extensive improvement in the diagnosis of illness, with machine learning models such as Convolutional Neural Networks leading the charge. This work introduces PneumoNet, a novel deep-learning model designed for accurate pneumonia detection from chest X-ray images. Pneumonia detection from chest X-ray images is one of the greatest challenges in diagnostic practice and medical imaging. Proper identification of standard chest X-ray views or pneumonia-specific views is required to perform this task effectively. Contemporary methods, such as classical machine learning models and initial deep learning methods, guarantee good performance but are generally marred by accuracy, generalizability, and preprocessing issues. These techniques are generally marred by clinical usage constraints like high false positives and poor performance over a broad spectrum of datasets.

A novel deep learning architecture, PneumoNet, has been proposed as a solution to these problems. PneumoNet applies a convolutional neural network (CNN) structure specifically employed for the improvement of accuracy and precision in image classification. The model employs several layers of convolution as well as pooling, followed by fully connected dense layers, for efficient extraction of intricate features in X-ray images. The innovation of this approach lies in its advanced layer structure and its training, which are optimized to enhance feature extraction and classification performance greatly. The model proposed here, PneumoNet, has been cross-validated and trained on a well-curated dataset that includes a balanced representation of normal and pneumonia cases.

Quantitative results demonstrate the model’s performance, with an overall accuracy of 98% and precision values of 96% for normal and 98% for pneumonia cases. The recall values for normal and pneumonia cases are 96% and 98%, respectively, highlighting the consistency of the model.

These performance measures collectively indicate the promise of the proposed model to improve the diagnostic process, with a substantial advancement over current methods and paving the way for its application in clinical practice.

Accurate preoperative grading of adult-type diffuse gliomas is crucial for personalized treatment. Emerging evidence suggests tumor cell infiltration extends beyond peritumoral edema, but the predictive value of radiomics features in these regions remains underexplored.

A retrospective analysis was conducted on 180 patients from the UCSF-PDGM dataset, split into training (70%) and validation (30%) cohorts. Intratumoral volumes (VOI_I, including tumor body and edema) and peritumoral volumes (VOI_P) at 7 expansion distances (1–5, 10, 15 mm) were analyzed. Feature selection involved Levene's test, t-test, mRMR, and LASSO regression. Radiomics models (VOI_I, VOI_P, and combined intratumoral-peritumoral models) were evaluated using AUC, accuracy, sensitivity, specificity, and F1 score, with Delong tests for comparisons.

The combined radiomics models established for the intratumoral and peritumoral 1-5mm ranges (VOI_1-5mm) showed better predictive performance than the VOI_I model (AUC=0.815/0.672), among which the VOI_1 model performed the best: in the training cohort, the AUC was 0.903 (accuracy=0.880, sensitivity=0.905, specificity=0.855, F1=0.884); in the validation cohort, the AUC was 0.904 (accuracy=0.852, sensitivity=0.778, specificity=0.926, F1=0.840). This model significantly outperformed the VOI_I model (p<0.05) and the 10/15mm combined models (p<0.05).

The peritumoral regions within 5 mm beyond the edematous area contain critical grading information, likely reflecting subtle tumor infiltration. Model performance declined with larger peritumoral distances, possibly due to increased normal tissue dilution.

The radiomics features of the intratumoral region and the peritumoral region within 5 mm can optimize the preoperative grading of gliomas, providing support for surgical planning and prognostic evaluation.

This study aimed to evaluate the impact of orthodontic extraction treatment on mandibular third molar impaction by measuring changes in angulation before and after treatment in patients receiving extraction versus non-extraction orthodontic therapy.

A retrospective analysis was conducted on 30 patients who completed fixed orthodontic treatment at the Department of Stomatology, Deyang People's Hospital, between 2018 and 2023. Patients were divided into two groups, with 15 included in the extraction group and 15 in the non-extraction group. Pre- and post-treatment orthopantomograms (OPGs) were analyzed, with each mandibular third molar quadrant considered an independent sample. Changes in the α-angle of mandibular third molars were compared between the groups.

The mean change in α-angle was -2.42° ± 8.32° in the non-extraction group and 4.85° ± 9.53° in the extraction group, with a statistically significant difference between the two groups (p < 0.05).

Whether orthodontic extraction treatment facilitates third molar eruption remains a topic of ongoing debate. Differences in conclusions across studies may be attributed to variations in sample selection, patient age, growth stage, anchorage strategies, and imaging methodology. Our study design attempted to control for these variables by matching participants by age, sex, and treatment duration, and by ensuring comparable baseline α-angle in both groups to minimize confounding. Further prospective studies based on three-dimensional imaging are still needed in the future to validate our conclusions.

Orthodontic treatment involving premolar extraction significantly improved the angulation of mandibular third molars compared to non-extraction treatment, potentially reducing impaction severity and enhancing eruption potential.

Anemia is a common blood disorder caused by a low red blood cell count, reducing blood hemoglobin. It affects children, adolescents, and adults of all genders. Anemia diagnosis typically involves invasive procedures like peripheral blood smears and complete blood count (CBC) analysis. This study aims to develop a cost-effective, non-invasive tool for anemia detection using eye conjunctiva images.

Eye conjunctiva images were captured from 54 subjects using three imaging modalities such as a DSLR camera, a smartphone camera, and a smartphone camera fitted with a 3D-printed spacer macro lens. Image processing techniques, including You Only Look Once (YOLOv8) and the Segment Anything Model (SAM), and K-means clustering were used to analyze the image. By using an MLP classifier, the images were classified as anemic, moderately anemic, and normal. The trained model was embedded into an Android application with geotagging capabilities to map the prevalence of anemia in different regions.

Features extracted using SAM segmentation showed higher statistical significance (p < 0.05) compared to K-Means. Comparing high resolution (DSLR modality) and the proposed 3D-printed spacer macrolens shows statistically significant differences (p < 0.05). The classification accuracy was 98.3% for images from a 3D spacer-equipped smartphone camera, on par with the 98.8% accuracy obtained from DSLR camera-based images.

The mobile application, developed using images captured with a 3D spacer-equipped modality, provides portable, cost-effective, and user-friendly non-invasive anemia screening. By identifying anemic clusters, it assists healthcare workers in targeted interventions and supports global health initiatives like Sustainable Development Goal (SDG) 3.

This study explored a generative image synthesis method based on diffusion models, potentially providing a low-cost and high-efficiency training data augmentation strategy for medical artificial intelligence (AI) applications.

The MedMNIST v2 dataset was utilized as a small-volume training dataset under low-performance computing conditions. Based on the characteristics of existing samples, new medical images were synthesized using the proposed annotated diffusion model. In addition to observational assessment, quantitative evaluation was performed based on the gradient descent of the loss function during the generation process and the Fréchet Inception Distance (FID), using various loss functions and feature vector dimensions.

Compared to the original data, the proposed diffusion model successfully generated medical images of similar styles but with dramatically varied anatomic details. The model trained with the Huber loss function achieved a higher FID of 15.2 at a feature vector dimension of 2048, compared with the model trained with the L2 loss function, which achieved the best FID of 0.85 at a feature vector dimension of 64.

The use of the Huber loss enhanced model robustness, while FID values indicated acceptable similarity between generated and real images. Future work should explore the application of these models to more complex datasets and clinical scenarios.

This study demonstrated that diffusion model-based medical image synthesis is potentially applicable as an augmentation strategy for AI, particularly in situations where access to real clinical data is limited. Optimal training parameters were also proposed by evaluating the dimensionality of feature vectors in FID calculations and the complexity of loss functions.